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Proteomics of a fuzzy organelle:interphase chromatinGeorg Kustatscher1,†, Nadia H�egarat2,†, Karen L H Wills1, Cristina Furlan1, Jimi-Carlo Bukowski-Wills1,

Helfrid Hochegger2,* & Juri Rappsilber1,3,**

Abstract

Chromatin proteins mediate replication, regulate expression, andensure integrity of the genome. So far, a comprehensive inventoryof interphase chromatin has not been determined. This is largelydue to its heterogeneous and dynamic composition, which makesconclusive biochemical purification difficult, if not impossible. As afuzzy organelle, it defies classical organellar proteomics and cannotbe described by a single and ultimate list of protein components.Instead, we propose a new approach that provides a quantitativeassessment of a protein’s probability to function in chromatin.We integrate chromatin composition over a range of differentbiochemical and biological conditions. This resulted in interphasechromatin probabilities for 7635 human proteins, including 1840previously uncharacterized proteins. We demonstrate the powerof our large-scale data-driven annotation during the analysisof cyclin-dependent kinase (CDK) regulation in chromatin. Quanti-tative protein ontologies may provide a general alternative tolist-based investigations of organelles and complement GeneOntology.

Keywords Cdk regulation; chromatin; machine learning; organelle;

proteomics

Subject Categories Cell Cycle; Chromatin, Epigenetics, Genomics &

Functional Genomics; Post-translational Modifications, Proteolysis &

Proteomics

DOI 10.1002/embj.201387614 | Received 6 December 2013 | Revised 12 January

2014 | Accepted 14 January 2014 | Published online 17 February 2014

EMBO Journal (2014) 33, 648–664

Introduction

Some of the first achievements of proteomics were to define the pro-

tein composition of organelles following isolation or quantitative

enrichment (Mootha et al, 2003; Schirmer et al, 2003). A second

generation of strategies has studied subcellular compartments based

on co-fractionation with marker proteins on density gradients

(Andersen et al, 2003; Dunkley et al, 2004; Foster et al, 2006).

Defining organelles in this way critically depends on biochemical

procedures and inherently introduces a series of purification arti-

facts. We have recently circumvented some of these by an approach

called Multiclassifier Combinatorial Proteomics (MCCP; Ohta et al,

2010). A common feature of these investigations is that they attempt

to completely separate genuine components from contaminants

through biochemical and/or bioinformatics approaches. Crucially,

the underlying assumption is that definite component lists can accu-

rately describe complex biological structures. In light of the dynamic

nature of organelles, an alternative concept may be needed.

Through examination of human interphase chromatin, we develop

an approach to capture the dynamic composition of biological struc-

tures, rather than enforcing static binary protein annotation.

Our analysis of interphase chromatin followed a three-stage

process. (i) We developed a new protocol to biochemically isolate

chromatin-enriched fractions. (ii) We employed MCCP (Ohta et al,

2010) with a refinement to encapsulate different degrees of functional

involvement of proteins in chromatin. (iii) We then derived for each

protein its probability of having a general chromatin-based function.

The final result is a quantitative protein ontology term “interphase

chromatin” that complements manually curated Gene Ontology (GO;

Ashburner et al, 2000) and network-extracted ontology (NeXO;

Dutkowski et al, 2013). We then apply this method to analyze changes

in chromatin mediated by Cdk1 and Cdk2 cyclin-dependent kinase

(CDK) activities in S-phase, and identify novel cell cycle-regulated

chromatin proteins that play a role in S-phase entry and progression.

Results and Discussion

A new chromatin enrichment procedure

As a first step, we optimized the proteomic coverage of human inter-

phase chromatin, that is, the DNA/histone fiber and all proteins

associating with it. For this, we developed a new procedure, which

we call chromatin enrichment for proteomics (ChEP). We fix

1 Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK2 Genome Damage and Stability Centre, University of Sussex, Brighton, UK3 Department of Biotechnology, Technische Universit€at Berlin, Berlin, Germany

*Corresponding author. Tel: +44 1273 877510; Fax: +44 1273 678121; E-mail: [email protected]**Corresponding author. Tel: +44 131 651 7056; E-mail: [email protected]†These authors contributed equally to this work.

The EMBO Journal Vol 33 | No 6 | 2014 ª 2014 The Authors.This is an open access article under the terms of the Creative Commons Attribution License,which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

648

Published online: February 16, 2014

proteins in chromatin by in vivo formaldehyde cross-linking and

remove non-covalently associated proteins by washing under

extremely stringent conditions (Fig 1 and Materials and Methods).

These initial conditions relate to standard chromatin immunoprecip-

itation (ChIP) experiments (Solomon et al, 1988) and were also

employed as starting point of a recent proteomic analysis of telo-

meres (D�ejardin & Kingston, 2009). However, our approach then

uses simple centrifugation to collect whole chromatin for subse-

quent mass spectrometric analysis of the associated cross-linked

proteins. This should allow quantitative analyses of processes that

affect chromatin globally.

In a representative proteomic analysis, we identified 3522 pro-

teins comprising typical chromatin-associated processes such as

transcription, histone modification, and DNA repair (Fig 1C). The

protocol enriches for chromatin factors in a considerably more

efficient way than the classical chromatin pellet (Fig 1D). None-

theless, nearly half of the proteins identified at this proteomic

scale have no apparent chromatin-related function. Additional

DNA-directed isolation steps, such as hydroxyapatite chromatogra-

phy, failed to reduce co-purifying unrelated proteins further (not

shown).

For mitotic chromosomes, we achieved a seemingly clear-cut

separation of genuine components and purification background

using MCCP (Ohta et al, 2010). However, such a separation may be

artificial in the case of interphase chromatin. A multitude of highly

dynamic and regulated biological processes take place in interphase

chromatin, such as replication, gene expression, and DNA repair.

Many proteins only associate with chromatin under specific physio-

logical conditions. For others, only part of their cellular pool is

active in chromatin. To emphasize these dynamic aspects of

interphase chromatin, we call it a “fuzzy” organelle. The ability to

describe these varying degrees of contribution is essential for an

accurate understanding of chromatin plasticity. Rather than qualita-

tively separating chromatin from background proteins in ChEP

fractions, we therefore aimed to quantify the contribution of each

protein toward chromatin. We hypothesized that MCCP would also

be able to address this.

A novel type of classifier to infer protein function

MCCP combines multiple “classifier” experiments, which individu-

ally separate two protein groups incompletely, into a powerful

super-ranking using a random forest (RF; Breiman, 2001) machine

learning algorithm. This algorithm learns to distinguish between

chromatin-like or chromatin-unlike behavior in classifier experi-

ments on the basis of well-described training proteins. Classifier

experiments used here include standard approaches such as com-

paring a chromatin-enriched fraction with whole-cell lysates or

other subcellular fractions (Fig 2A–C and Table 1). We applied six

“biochemical classifiers” that infer function from physical associa-

tion and invariably carry the risk of purification artifacts (contami-

nants and losses). Moreover, these classifiers do not take into

account different physiological states of chromatin and conse-

quently cannot integrate regulated changes in chromatin composi-

tion. To circumvent this problem, we developed an alternative type

of classifier experiment. We compared ChEP preparations differing

as a result of regulated physiological changes within the cell. This

“biological classifier” approach aims to reduce purification artifacts

while at the same time encapsulating biological complexity (Fig 2E).

Function is inferred by machine learning from co-behavior with

known reference proteins. In our case, these are 486 proteins linked

to chromatin and 582 not (see Materials and Methods).

Our biological classifiers rely on global, systemic perturbations.

This ensures that a broad range of chromatin processes is affected

and in many ways, even though such changes might be subtle. For

example, ChEP preparations from cells treated with or without

TNF-a show subtle, global changes that affected chromatin and

non-chromatin proteins differently (median 1.12-fold difference;

A B C D

Figure 1. Chromatin enrichment for proteomics (ChEP).

A Outline of the ChEP procedure, see Materials and Methods for details.B SDS–PAGE gel of a typical chromatin fraction obtained using this procedure, compared to a whole-cell lysate and a classical chromatin pellet.C Proteomic analysis of a typical ChEP chromatin sample. 3522 proteins were identified and classified manually according to their function. The number of proteins

per category is indicated in brackets.D SILAC-based quantitative proteomics comparing ChEP chromatin with a classical chromatin pellet, demonstrating that ChEP enriches for chromatin players more

efficiently. The 1024 known chromatin proteins (red) and 1706 proteins with no expected chromatin function (blue) were annotated manually based on literatureevidence. Uncharacterized proteins are not shown.

Georg Kustatscher et al Proteomics of a fuzzy organelle: interphase chromatin The EMBO Journal

ª 2014 The Authors The EMBO Journal Vol 33 | No 6 | 2014 649


Fig 2F). We subsequently added data on chromatin-enriched frac-

tions from another 18 different biological conditions, including cell

types, cell cycle phases, and drug treatments (see Table 1 for full

list). In all these cases, we observe general, coordinated alterations,

such that the densities of chromatin and non-chromatin proteins

vary slightly throughout all SILAC plots (Fig 2F–H). Even though

detectable through the high accuracy of quantitative proteomics

(Ong et al, 2002), such small bulk changes are usually overlooked

or dismissed in favor of large changes in few proteins. For some

experiments, no coordinated differences between chromatin and

non-chromatin proteins were observed, for example protein turn-

over (not shown), and these were not included here.

We integrate co-fractionation changes in response to many

in vivo perturbations instead of suggesting function from biochemi-

cal co-fractionation alone. As a consequence, the composition of the

organelle is defined in its native environment. Accordingly, abun-

dant contaminants of chromatin purifications are correctly identified

as false positives by biological classifiers, since these proteins do

not respond to physiological changes in the same way as genuine

chromatin components (Supplementary Fig S1). Note that a virtu-

ally unlimited number of biological classifiers can be conceived.

Even treating cells with TNF-a for 5 min rather than 10 min pro-

vides additional information (Supplementary Fig S2). Importantly,

perturbations do not need to target the structure in question directly

or selectively, as long as they induce global biological changes that

affect the structure.

An integrated chromatin score

The output, an integrated chromatin score, was validated using

5795 proteins that we manually annotated as either “chromatin pro-

teins” (any reported function on chromatin) or “non-chromatin

proteins” (well-characterized proteins without indication of involve-

ment with chromatin; Fig 2D). Notably, the combined set of global

perturbation experiments discriminates chromatin from non-chro-

matin players better than a classic biochemical enrichment experi-

ment, such as comparing a chromatin fraction with a whole-cell

lysate (Supplementary Fig S1). For the remainder of this study, we

integrated all experiments that showed some bulk separation (see

Table 1). This optimized performance as judged by receiver operat-

ing characteristic (ROC)-like curves (Fig 2D) and maximized the

number of proteins observed.

From machine learning score to interphasechromatin probability

A protein with integrated chromatin score of 0.8 received a chroma-

tin vote from 80% of the trees in the RF. The score provides a rank-

ing but gives no indication on how likely the protein has a

chromatin function. To provide dimension and scale, we calibrated

the score distribution making use of the 5795 annotated evaluation

proteins in our dataset. We calculated the fraction of proteins with

reported chromatin functions among all characterized proteins

A B C D

E F G H

Figure 2. Defining interphase chromatin through biochemical or biological classifiers.

A Traditional biochemical classifiers compare chromatin with other subcellular fractions.B, C SILAC plots showing enrichment of known chromatin (red) over non-chromatin proteins (blue) for two such experiments. The distributions of chromatin and non-

chromatin proteins are displayed as overlaid graphs.D Individual experiments are integrated by machine learning for a more powerful distinction of chromatin and non-chromatin proteins, as assessed by receiver

operating characteristic-like curves.E–H Schematics and SILAC plots of representative biological classifiers showing the uneven distribution of chromatin and non-chromatin proteins in chromatin

fractions prepared from distinct physiological conditions (see Table 1 for full list of experiments). The following number of proteins were quantified and plottedafter median normalization: (B), 1441 chromatin proteins/2882 non-chromatin proteins; (C), 1373/2636; (F), 1130/1709; (G), 933/1193; (H), 1156/1774.

The EMBO Journal Proteomics of a fuzzy organelle: interphase chromatin Georg Kustatscher et al

The EMBO Journal Vol 33 | No 6 | 2014 ª 2014 The Authors650


Table 1. Experiments used to infer the composition of interphase chromatin

ID Sample 1 (SILAC light) Sample 2 (SILAC heavy) Sample 3 (SILAC medium) Cell line Proteins Comment

Biochemical classifier experiments (comparing ChEP chromatin with other biochemical fractions)

a Whole-cell lysate ChEP chromatin / MCF-7 5227 See note 1

b ChEP chromatin Whole-cell lysate / HeLa 5650 See note 1

c Whole-cell lysate ChEP chromatin / HepG2 2262

d Nuclei lysate ChEP chromatin / HeLa 5121

e Chromatin pellet ChEP chromatin / HeLa 4852

f Chromatin pellet ChEP chromatin / HepG2 1676

g Whole-cell lysate ChEP chromatin / HeLa 5231 Label-swap of (b)

Biological classifier experiments (comparing ChEP fractions from different biological conditions)

a Untreated TNF-a, 5 min / HeLa 3789

b Untreated TNF-a, 10 min / HeLa 3658

c Untreated TNF-a, 30 min / HepG2 3615

d a-Amanitin DMSO Trichostatin A MCF-7 1546

e Untreated Camptothecin / U20S 2769

f Untreated Ionizing radiation, 10 Gy / U20S 2793

g Untreated Ionizing radiation, 30 Gy / U20S 2742

h Untreated E2 and 4-OHT E2 MCF-7 2768 See note 2

i From HEK293 cells From HepG2 cells / / 3719

J Cell cycle Gl/S Cell cycle phase M Cell cycle phase G2 HeLa 3720 See note 3

k 10% rat serum 1 10% dialyzed FCS / HepG2 3038 See note 4

l 10% rat serum 2 10% dialyzed FCS / HepG2 3297 See note 4

m Untreated 1 lg/ml doxycycline / HeLa 2271 See note 5

n Untreated 1 lg/ml doxycycline 0.1 lg/ml doxycycline HeLa 1843 See note 5

o a-Amanitin DMSO Trichostatin A MCF-7 1517 Replica of (d)

p Cell cycle phase G2 Cell cycle Gl/S Cell cycle phase M HeLa 4088 See note 6

q Untreated E2 and 4-OHT E2 MCF-7 3394 Replica of (h)

r E2 and 4-OHT E2 Untreated MCF-7 2883 Label-swap of (h)

s E2 Untreated E2 and 4-OHT MCF-7 1301 Label-swap of (h)

t E2 and 4-OHT E2 Untreated MCF-7 3481 Label-swap of (h)

u E2 Untreated E2 and 4-OHT MCF-7 2914 Label-swap of (h)

v Untreated 1 lg/ml doxycycline / HeLa 3123 Replica of (m)

w 1 lg/ml doxycycline Untreated / HeLa 3187 Label-swap of (m)

Additional classifier experiments

a Purified w/o RNase Purified with RNase / HepG2 1749 See note 7

b Cell cycle Gl/S Cell cycle phase M / HeLa 1183 5 min fixation

c Cell cycle Gl/S Cell cycle phase M / HeLa 1196 10 min fixation

d Cell cycle Gl/S Cell cycle phase M / HeLa 1273 15 min fixation

e Cell cycle Gl/S Cell cycle phase M / HeLa 1372 20 min fixation

Note 1 Mixed 1:4 to increase detection of chromatin factors (affects all proteins, so no impact on classifier performance).Note 2 E2 is 17-b-estradiol; 4-OHT is 4-hydroxytamoxifen.Note 3 Cells arrested by thymidine (Gl/S), RO-3306 (G2), nocodazole (M).Note 4 Rat serum replaced FCS in cell culture medium.Note 5 Stable cell line expressing macroH2Al.l from a doxycycline-induced Tet-ON promoter.Note 6 Label-swap replica of (j), but only fixed for 5 min with formaldehyde.Note 7 Standard ChEP procedure includes RNase treatment, see Materials and Methods.




within score windows. We described the result as a sigmoid func-

tion (Fig 3A, see Materials and Methods for details). In this way, we

integrate knowledge on proteins with similar scores into the proba-

bility of any given protein to have a chromatin function. This trans-

lation is robust and reproducible (Supplementary Fig S3). A

calibrated score of 0.8 for instance means that eight of 10 reference

proteins with this value have a reported chromatin function, thus

providing a probability for the function of this protein. We refer to

this value as interphase chromatin probability (ICP; Fig 3B, Supple-

mentary Table 1). ICPs provide a general annotation on how similar

a protein behaves experimentally to archetypal chromatin proteins.

We provide ICPs for 7635 human proteins and protein isoforms,

including the 5795 evaluation proteins (1823 proteins with literature

evidence linking them to chromatin and 3972 non-chromatin pro-

teins) and 1840 previously uncharacterized proteins. Proteins were

classified as “uncharacterized” based on absence of literature but

also had low GO coverage and weak domain-based prediction (Sup-

plementary Fig S4). Of the 1840 uncharacterized proteins described

in this study, 576 have a chromatin probability >0.5, indicating that

hundreds of chromatin components are presently still uncharacter-

ized. The large number of novel chromatin proteins is in line with a

recent report that used alternative technology to test more than 100

proteins and found 42 previously unknown chromatin components

(van Bemmel et al, 2013). ICPs integrate large-scale data for quanti-

tative gene function prediction and can help systematically fill cur-

rent annotation gaps.

ICPs are consistent with the function of protein domains

To validate ICPs, we performed several tests based on literature

knowledge and bioinformatics and finally applied the method to elu-

cidate cell cycle regulation of chromatin. As a first validation step,

we investigated the correlation between ICPs and the presence of

protein domains that have been linked to interphase chromatin

(Supplementary Fig S5). As expected, proteins with canonical chro-

matin domains (e.g., chromo, bromo, JmjC) invariably have high

ICPs. Conditional and regulated chromatin proteins such as tran-

scription factors with sequence-specific DNA-binding domains

indeed cover a broad range of ICPs. Proteins with a Ras domain

have no reported chromatin involvement and receive low ICPs. This

suggests that ICPs can capture a dynamic range of involvement in

chromatin.

ICPs capture diverse biological behavior of proteins

We next investigated whether ICPs accurately reflect the biological

behavior of well-described proteins and their complexes. Typical

chromatin-associated protein complexes such as MCM2-7 and

FACT have consistently high ICPs for all their subunits (Fig 4A).

In contrast, subunits with multiple functions correctly have differ-

ent ICP values from core subunits. This includes the MLL histone

methyltransferase subunits Dpy-30L (Xu et al, 2009) and HCF2

(Johnson et al, 1999; Fig 4A) and multiple NuRD components

(Supplementary Fig S6). Different isoforms of NuRD subunits with

redundant function receive similar ICPs, indicating large accuracy

of ICP values. Ribosomes, commonly found contaminants in bio-

chemical purifications, have low ICP values (Fig 4B). This indi-

cates that ICPs successfully integrate biological rather than

biochemical behavior. ICPs also match the dynamic chromatin

association of the condensin complex (Fig 4A). In interphase, con-

densin I subunits diffuse into the cytoplasm (very low ICP) and

condensin II subunits remain nuclear with a low chromatin affinity

(Gerlich et al, 2006; medium ICP). Common condensin subunits

A

B

Figure 3. Toward a probabilistic chromatin definition.

A Using 5795 proteins of known function, the percentage of chromatin (red)and non-chromatin proteins (blue) was calculated for overlappingwindows (e.g., gray box) of the random forest (RF) machine learning score.Fitting a sigmoid curve through the percentages translates the RF scoreinto interphase chromatin probabilities (ICPs). 1823 evaluation proteinsare known chromatin players, while 3972 have no expected chromatinfunction.

B Scatterplot showing ICPs for 7635 human proteins (the 5795 evaluationproteins and 1840 uncharacterized proteins). Pie charts show proteincategories above and below ICP 0.5, including uncharacterized proteins(white).




show an intermediate behavior. This is consistent with ICPs being

a parameter that describes the average behavior when multiple

pools are present. Similarly, Cdk1 has a low ICP (0.15) as its main

pool is bound to cytoplasmic cyclin B, while a minor fraction com-

petes with Cdk2 for nuclear cyclin A and E and acts on chromatin

(Santamar�ıa et al, 2007). Different from ribosomes, factors of ribo-

some biogenesis associate with pre-rRNA co-transcriptionally and

thus have some chromatin association. This is reflected in interme-

diate to high ICP values (Fig 4B). Interestingly, splicing factors

show a large spread from low to high ICP values. This distribution

is not random; all Sm and LSm proteins have low ICP values,

while SR-rich splicing factors, which can act co-transcriptionally

(Zhong et al, 2009), consistently have high ICP values (Fig 4C).

Similarly, ICPs allow distinguishing canonical from conditional

chromatin proteins. For example, SMAD and STAT transcription

factors are normally absent from chromatin due to their signal-

dependent nuclear localization and have consistently low ICPs

(Fig 4A). In conclusion, ICPs provide a quantitative annotation

that captures the subtle biological behavior of diverse proteins and

functions, rather than providing classical “all or nothing” scores to

distinguish between true and false positives.

ICPs as quantitative annotation of the multifunctional proteome

As a final test, we asked whether ICPs could identify predominantly

chromatin-based proteins among those 248 proteins in our dataset

that are both cytoplasmic and chromosomal according to the GO

database (Ashburner et al, 2000). ICPs can successfully reveal these

proteins’ main activities as shown by the examples in Fig 5. Looking

at the most extreme ICP values, 16 proteins with highest ICP are

well-described chromatin proteins, while 13 proteins with lowest

ICP have a main function elsewhere. Note that proteins with low

chromatin ICP are not indicating GO artifacts, for example septin

filaments interact with kinetochores during mitosis (Spiliotis et al,

2005; Zhu et al, 2008). This demonstrates that ICPs may help to

address one of the large problems of protein annotation. Protein

annotation databases are challenged by an increasing amount of

data on proteins leading to an accumulation of proteins with multi-

ple, apparently unrelated, annotations. For example, according to

GO, 40% of all human nuclear proteins are also found in the cyto-

plasm. Many of these proteins will be multifunctional. However,

increasingly sensitive analyses will decrease the value of protein

localization for function prediction, for example chromatin proteins

may be observed while they are translated in the cytoplasm. This

ultimately reduces the value of such annotations. Quantitative

protein ontologies, as suggested here, have the potential to solve

these issues by providing a probabilistic dimension to protein

annotations.

A B C

Figure 4. Interphase chromatin probabilities describe chromatin plasticity.

A Protein complexes can have uniform interphase chromatin probabilities (ICPs; MCM2–7, FACT) but also heterogeneous ICPs (MLL, condensin) in agreement withadditional roles of individual subunits. Consistently low ICPs are assigned to cytoplasmic, signal-dependent SMAD/STAT transcription factors.

B, C ICPs also set apart ribosome biogenesis, which happens in nucleoli as part of or vicinal to chromatin, from core ribosome components (B), as well as core splicingfactors (Sm/LSm) from SR proteins, which can act co-transcriptionally (C).

Data information: The number of proteins in each group is indicated in brackets.

Figure 5. Interphase chromatin probabilities (ICPs) as quantitativeannotation for multifunctional proteins.

ICP distribution of 248 proteins with both cytoplasmic and chromosomallocalization according to Gene Ontology (red/blue circles) reveals a protein’score function.




ICPs as adjustable focus for Cdk-dependent chromatin regulation

ICPs could be used for guidance when looking for bona fide chro-

matin proteins. ICPs do not define specific chromatin functions of

individual proteins. Therefore, we envision ICPs as a form of

large-scale data-derived and quantitative GO term to allow focus-

ing other datasets onto chromatin function. We undertook two

studies to exemplify this. First, we analyzed changes in chromatin

composition driven by Cdk-dependent cell cycle progression

through S-phase (Fig 6A). Initiation and completion of DNA repli-

cation has a major impact on chromatin (Khoudoli et al, 2008),

but how core chromatin processes are cell cycle regulated in

somatic cells remains poorly understood. To address this question,

we conducted a quantitative proteomics study that took advantage

of an analogue-sensitive mutation in Cdk1 that we previously

established in wild-type (WT) and Cdk2-knockout chicken DT40

cells (Hochegger et al, 2007). This mutation allows the rapid and

highly specific inactivation of Cdk1 by the bulky ATP analogue

1NMPP1. Neither Cdk1 nor Cdk2 is required for S-phase progres-

sion, while inactivation of Cdk1 in Cdk2-knockout cells causes a

complete block of DNA replication initiation and S-phase progres-

sion. Accordingly, after 2 hours of 1NMPP1 treatment, we

observed an arrest in G1- and S-phase of the cell cycle only in

Cdk2-knockout cells, while Cdk2-expressing WT cells proceeded to

G2-phase (Fig 6A). Below, we will indicate the combination of

Cdk1 and Cdk2 as “Cdk.”

We compared ChEP chromatin obtained from cdk1as and

cdk1as/cdk2�/� cells 2 h after Cdk1 inactivation. Statistical analy-

sis of four independent experiments (including 2 label swaps)

identified 135 proteins that showed a significant change in SILAC

ratio among a total of 2402 proteins quantified in at least three

experiments (Supplementary Fig S7 and Supplementary Table 2).

Of these candidates, 114 had a human one-to-one ortholog (Sup-

plementary Tables 2 and 3). As expected, Orc1, but not other Orc

subunits, was strongly regulated by Cdk (M�endez et al, 2002) and

so was the cohesin complex. However, a large number of

cytoplasmic proteins were also affected by Cdk activity. This

hampered the selection of novel candidates that participate in

Cdk-regulated core chromatin processes based on SILAC ratios

alone. We therefore took advantage of ICPs to filter our dataset of

Cdk-regulated proteins according to their predicted functional

association with chromatin (Fig 6B and Supplementary Table 3).

We performed an in-depth protein network analysis using two dif-

ferent chromatin probabilities (Figs 6D and 7). A broad analysis

was performed with proteins of ICP at least 0.1, and a more strin-

gent filter was applied by using an ICP of 0.5. The low stringency

ICP 0.1 filter removed already 50 of the 114 candidate proteins,

most of which have no expected chromatin function (Fig 6C). This

provided a clearer view of a complex dataset that includes loosely

chromatin-associated processes such as NFjB and lymphocyte

BCR signaling, SMN complex, cytokinesis and kinetochore compo-

nents, as well as Cdk1 itself (Fig 7). The stringent filter provided

a network that closely focuses on core chromatin processes such

as chromatin modification, telomere maintenance, and nuclear

structural organization (Fig 6D). Both networks provide useful

insights into the cell cycle regulation of chromatin and reveal a

variety of novel proteins that appear to be regulated in a

Cdk-dependent manner.

ICPs pinpoint novel Cdk-regulated chromatin players

To demonstrate that we have indeed identified novel cell cycle-regu-

lated chromatin factors, we experimentally validated five novel can-

didates for cell cycle-regulated chromatin association. We chose

these candidates based on their combination of suggestive ICPs and

high SILAC ratios. One is a known chromatin protein, the myc tran-

scription regulator FUBP1 [ICP 0.62; reviewed in (Zhang & Chen,

2013)]. The PHF family protein PHF6 (ICP 0.93) is an uncharacter-

ized protein implicated in B€orjeson–Forssman–Lehmann syndrome

(Lower et al, 2002). Smek2 (ICP 0.65) is a regulatory subunit of PP4

with cytoplasmic and nuclear localization (Chowdhury et al, 2008).

Cdc20 is a regulatory subunit of the anaphase-promoting complex

(Yu, 2007) that has so far not been implicated to function on inter-

phase chromatin, but has a surprisingly high ICP (ICP 0.57) and

shows a strong Cdk-dependent change in the SILAC experiments.

We also included Cdk1 that has a low chromatin probability of 0.15,

but that still is likely to have some chromatin-associated functions

(Santamar�ıa et al, 2007). We confirmed Cdk-dependent chromatin

association of these proteins in human cells, before and after treat-

ment with the Cdk inhibitor roscovitine, by Triton extraction and

immunofluorescence. In each case, the candidate protein behaved

as predicted by the SILAC data, either enhancing or decreasing their

chromatin affinity after Cdk inhibition (Fig 8). In the case of PHF6,

we observed a sequestration of the protein in the nucleolus, and a

release onto the non-nucleolar chromatin after Cdk inhibition. In all

other cases, we observed a simple increase or decrease in chromatin

association following roscovitine treatment that corresponded with

the respective SILAC ratio in the proteomic dataset.

The observed Cdk-regulated chromatin association of these pro-

teins could point to a role in cell cycle progression. We tested this

hypothesis by analyzing the cell cycle profile following siRNA-medi-

ated depletion of Smek2, PHF6 and FUBP1 (Fig 9A). In all three

cases, we observed significant changes in the cell cycle profile after

72 hours of depletion of the candidates (Fig 9A). PHF6 knockdown

caused an increase in the G1 population, while Smek2 and FUBP1

depletion caused a significant change in the replicative proportion

of cells, suggesting functions for these proteins in S-phase progres-

sion (Fig 9B). Of these, the PP4 regulatory subunit Smek2 had the

most dramatic phenotypes, suggesting important novel roles in cell

cycle control. We then took advantage of the ICP resource to ana-

lyze a further set of five proteins with no known chromatin-asso-

ciated function that nevertheless showed ICP values > 0.5

(Fig 9D,E) and a Cdk-dependent change in the SILAC ChEP exper-

iments. The combination of high ICP and SILAC ratio could sug-

gest that some of these proteins play a role in interphase

progression. Indeed, we found significant cell cycle phenotypes

for two of these five genes after knockdown in RPE cells. Both

TMA16 and OGFOD1, both of which are largely uncharacterized

proteins with no known function in cell cycle progression and

chromatin biology (Saito et al, 2010; Wehner et al, 2010), show a

strong reduction in cells in S-phase after 72 hours of depletion,

suggesting the activation of cell cycle checkpoints in G1 and/or

G2 or a direct block in replication initiation.

Our approach to observe changes in chromatin association fol-

lowing Cdk inhibition does not allow us to distinguish between

direct Cdk-regulated targets and proteins that are indirectly affected

by changes in cell cycle progression. To test whether some of the




A

D

B C

Figure 6. Using interphase chromatin probabilities (ICPs) to analyze Cdk-dependent changes in S-phase chromatin.

A 108 cdk1as and cdk1as/cdk2�/� DT40 cells, cultured in SILAC media, were incubated in 10 lM of 1NMPP1 for 2 h. Propidium iodide staining and flow cytometryanalysis show the cell cycle profiles in the absence or presence of 1NMPP1. Cells were then collected and chromatin was extracted using ChEP method.

B The average SILAC ratios of 1652 proteins quantified in at least three Cdk experiments are plotted against their ICP. Filled circles represent significant Cdk outliers(P < 0.05). Known chromatin proteins are red, known non-chromatin factors blue, and uncharacterized proteins gray.

C Bar chart showing how many of 114 conserved Cdk outliers fall in various ICP ranges. Color code as in (B).D Protein network of candidates with an ICP > 0.5, all of which constitute core chromatin pathways. Candidates with increased chromatin binding after Cdk inhibition

are shown in green, and candidates with decreased affinity in red. Gray signifies proteins that are in a known complex, but absent from our data. Bold borders marksignificant outliers. Proteins with published Cdk phosphorylation sites are shown as diamonds.




candidate proteins that we identified in our SILAC screen are

directly targeted by Cdks, we generated recombinant bacterially

expressed proteins for PHF6, FUBP1, and Smek2 and subjected them

to in vitro kinase assays using recombinant Cdk2/cyclin A

complexes. PHF6 and Smek2 were readily phosphorylated in these

in vitro reactions (Fig 9F,G), while FUBP1 did not appear to be a

Cdk substrate (Fig 9H). We identified potential conserved Cdk phos-

phorylation sites in both PHF6 (S154 and S155) and Smek2 (S840)

that also were identified as phosphorylated residues in previous pro-

teomic screens (Hornbeck et al, 2012). Mutation of these serine

residues to alanine markedly reduced the Cdk-dependent phosphor-

ylation of these proteins, suggesting that these residues are directly

phosphorylated by Cdks (Fig 9F,G). We generated phospho-specific

antibodies to monitor the phosphorylation of Smek2 S840 phosphor-

ylation in cells. These antibodies were highly specific for S840 phos-

phorylation because they strongly cross-reacted with recombinant

WT Smek2, but not with the S840A mutant Smek2 after incubation

with ATP and Cdk2/cyclin A, as judged by immunoblotting (Fig 9I).

We then used this antibody to test the Cdk-dependent phosphoryla-

tion of Smek2 S840 in DT40 cells. We found that inhibition of Cdk1

in Cdk2-knockout cells, but not cells expressing Cdk2, caused a

rapid loss of signal in immunoblots probed with the phospho-

specific S840 Smek2 antibody (Fig 9J). These data suggest that

Smek2 is indeed a target of S-phase Cdk.

In summary, this proteomic screen in combination with ICP-

based filtering provides a validated resource of a large variety of

novel cell cycle-regulated chromatin-associated proteins. Our

approach led us to discover a novel functional interplay between

interphase Cdks and the chromatin association of a variety of novel

candidate proteins such as Cdk1 itself, the APC/C regulatory subunit

cdc20, the PP4 regulatory subunit Smek2, the helicase FUBP1, and

the PHD domain-containing zinc finger protein PHF6. We show

novel cell cycle phenotypes for the latter three candidates and find

that at least two of these proteins are direct targets of interphase

Cdks. We further used ICPs to highlight potential novel chromatin-

associated cell cycle regulators among five of the least characterized

Figure 7. Protein network of candidates with an interphase chromatin probabilities (ICP) > 0.1.

Candidates with increased chromatin binding after Cdk inhibition are shown in green; candidates with decreased affinity in red. Gray signifies proteins that are in aknown complex, but absent from our data. Bold borders mark significant outliers, and colors indicate the range of ICP values. Known Cdk substrates are extracted fromthe Web site PhosphositePlus (www.phosphosite.org) and are shown as diamonds. To build the network, some pathways required the addition of outliers with an ICPbelow 0.1. All protein interactions are referenced in Supplementary Table 5.




http://www.phosphosite.org

proteins in the SILAC candidate list. This approach is validated by

the discovery of novel cell cycle phenotypes following siRNA-medi-

ated depletion of two out of these five candidates, namely TMA16

and OGFOD1. Further studies are necessary to elucidate the exact

impact of this regulatory network on S-phase progression.

In a parallel study, we compared newly replicated chromatin

with post-replicative chromatin using quantitative proteomics

(Alabert et al, 2014). As expected, replication factors were

strongly enriched in new chromatin. However, among the other

candidates, we identified many non-canonical chromatin proteins,

as was the case for our Cdk study. Again, ICPs provided an

additional dimension that allowed us to focus on canonical

chromatin proteins. When cloning three candidates with high

ICPs and four with low ICPs, we found that all seven proteins

behaved as expected and ultimately characterized a novel

replication factor.

Conclusion

Subcellular structures in general and not just interphase chromatin

are highly interactive and dynamic and cannot fully be described as

static, isolated features. Instead of providing a simple binary cut-off

approach, we provide a means to filter datasets in a biologically

meaningful manner that catches the dynamic nature of many pro-

cesses. Our new concept has the potential to describe the plasticity

A

E

B C D

Figure 8. Validating Cdk-dependent chromatin association changes of selected outliers.

A–E Validation of changes in chromatin affinity following Cdk inhibition of indicated candidates (2 h, 50 lM roscovitine) using Triton extraction andimmunofluorescence (IF) in HeLa cells. Quantification of IF data was performed by measuring changes in signal intensities in Image J (A–D) and qualitativeanalysis of nucleolar staining (E). In each experiment, n > 50 nuclei were analyzed for each condition in three independent experiments (P indicates P value fromMann–Whitney test).




A

D E

H I

J

F

G

B C




of many biological structures by overcoming a number of limita-

tions in current proteomic approaches. First, biological classifiers

bypass the need for stringent biochemical purification and raise

organelle definition from physical association to biological co-

behavior. Next, the integration of many such classifiers into a prob-

abilistic output reflects the dynamic composition of organelles.

Probabilities could be valuable additions to public protein databas-

es, essentially complementing manual, qualitative annotations with

large-scale data-driven and quantitative information. In principle,

these could be automatically updated from incoming large-scale

experimental evidence. The resource of 7635 ICPs provided here

can immediately focus candidate lists on archetypal chromatin func-

tions and boost our understanding of chromatin-based processes as

our biological validation demonstrates. We envisage that our

approach can be applied to any organelle, compartment, or other

complex biological structure.

Materials and Methods

Cell culture, SILAC labeling, and treatments

Human cell lines were grown in DMEM-based medium free of argi-

nine and lysine (custom-made by AthenaES, Baltimore, MD, USA).

The medium was supplemented with 10% dialyzed fetal bovine

serum (Invitrogen), 0.2 mM arginine, and 0.79 mM lysine. For SI-

LAC labeling (Ong et al, 2002) of human cells, medium was supple-

mented with unlabeled amino acids (light label), with arginine:HCl,

13C6 (Cambridge Isotope Laboratories), and lysine:HCl, 4,4,5,5-d4

(Sigma) for the medium label, or with arginine:HCl 13C6, 15N4 and

lysine:HCl, 13C6, 15N2 (Sigma) for the heavy label. For biological

classifiers, cells were treated as described in Table 1. HeLa,

HEK293, and HepG2 cells were kind gifts from E. Schirmer,

I. Stancheva, and A. Ladurner, respectively. HeLa Tet-On cells

expressing a Flag- and HA-tagged version of macroH2A have been

described (Timinszky et al, 2009). Cdk1as and cdk1as/cdk2�/�

DT40 cells were cultured for six cell cycles in SILAC RPMI 1640

medium (Thermo Fisher Scientific, Bremen, Germany) containing

10% dialyzed fetal bovine serum (F0392; Sigma), 2 mM L-gluta-

mine, 0.1% b-mercaptoethanol, 100 U/ml penicillin, and 0.1 mg/ml

streptomycin. In light conditions, the media were supplemented

with 30 lg/ml L-arginine (Sigma) and 100 lg/ml L-lysine (Sigma)

and in heavy conditions with 30 lg/ml L-arginine:HCl (U-13C6) and

100 lg/ml L-lysine:2HCl (4,4,5,5-D4) (Cambridge Isotope Laborato-

ries). These cells were incubated at 39°C in a humidified cell culture

chamber with 5% CO2. 108 cells were treated with 10 lM of

1NMPP1 (gift from Dr. Hans Streicher, University of Sussex) for 2 h.

All cell lines were tested for full label incorporation and lack of

arginine to proline conversion. We routinely test our cell lines for

mycoplasma contamination.

Chromatin enrichment for proteomics (ChEP)

Typically, two 150-cm2 petri dishes of human cells were grown in

SILAC light and heavy medium, respectively. 108 DT40 cells were

used per experimental condition. Cells were washed with PBS, then

cross-linked in vivo with 1% formaldehyde in PBS for 10 min at

37°C, as for chromatin immunoprecipitation experiments (Solomon

et al, 1988). Cross-linking was stopped by the addition of glycine to

a final concentration of 0.25 M and incubation for 5 min at room

temperature (RT). Cells were rinsed with PBS, scraped off, and har-

vested by centrifugation in 50 ml PBS (5 min, 423 g). The pellets

were resuspended in 1 ml ice-cold cell lysis buffer (25 mM Tris, pH

7.4, 0.1% Triton X-100, 85 mM KCl; Roche protease inhibitors) and

transferred to 2-ml test tubes. They were homogenized by carefully

pipetting up and down with a 200-ll pipette tip. Lysed cells/nuclei

were pelleted in a bench-top centrifuge at 2300 g for 5 min at 4°C.

The supernatants (cytoplasm) were transferred to new tubes, and

relative protein concentrations of light and heavy SILAC samples

were estimated by Bradford assay. The nuclei pellets were resus-

pended in 500 ll cell lysis buffer containing 200 lg/ml RNase A

and incubated for 15 min at 37°C. Equal amounts of nuclei from

light and heavy SILAC samples were then pooled as estimated on

the basis of cytoplasmic extract quantitation. Nuclei were collected

by centrifugation at 2300 g for 10 min at 4°C. They were then resus-

pended in 500 ll of SDS buffer (50 mM Tris, pH 7.4, 10 mM EDTA,

4% SDS; Roche protease inhibitors) using hydrophobic pipette tips

and incubated for 10 min at RT. Next, 1.5 ml of urea buffer (10 mM

Tris, pH 7.4, 1 mM EDTA, 8 M urea) was added and mixed by

inverting the tube multiple times. Centrifugation in a table-top cen-

trifuge at full speed for 30 min (25°C) yielded a transparent, gel-like

pellet. Using hydrophobic pipette tips, the pellet was resuspended

again in 500 ll SDS buffer, mixed with 1.5 ml of urea buffer, and

centrifuged for 25 min at full speed (25°C). The pellet was washed

once more in 2 ml of SDS buffer. Finally, the pellet was covered

with 0.5 ml of storage buffer (10 mM Tris, pH 7.4, 1 mM EDTA,

25 mM NaCl, 10% glycerol; Roche protease inhibitors) and soni-

cated in ice water to solubilize chromatin completely. After a final

centrifugation step (30 min, full speed, 4°C), the amount of solubi-

lized chromatin in the supernatant was quantified by Bradford

assay. Formaldehyde cross-links were reversed by incubation with

SDS–PAGE loading buffer for 30 min at 98°C.

Figure 9. Cell cycle changes following depletion of candidate proteins and Cdk-dependent phosphorylation of Smek2 and PHF6.

A Propidium iodide (PI)/EdU staining and FACS analysis of HeLa cell lines after 72 hours of depletion with indicated siRNAs.B Quantification of FACS cell cycle analysis in (A) showing the mean value and standard deviation of three independent experiments.C Verification of siRNA depletion of PHF6, FUBP1, and Smek2 by immunoblotting.D Quantification of the EdU incorporating fraction of RPE cells following 72 hours of depletion with siRNA pools against the uncharacterized proteins with high

interphase chromatin probability (ICP) values (THYN1, ICP = 0.91; TMA16, ICP = 0.9; OGFOD1, ICP = 0.86; C3ORF37, ICP = 0.83; LYAR, ICP = 0.61).E FACS/PI histograms and EdU incorporation dot blots of cells 72 h after transfection with control, TMA16, and OGFOD1 siRNAs.F–H In vitro kinase assays with purified PHF6, Smek2, and FUBP1 and the indicated Smek2 and PHF6 mutant proteins.I Immunoblotting of recombinant WT and S840A Smek2 before and after incubation with ATP and Cdk2/cyclinA.J Immunoblotting of extracts of cdk1as and cdk2�/�/cdk1as DT40 cells at indicated time points following 1NMPP1 addition.

◀




Preparation of other biochemical fractions

To prepare a nuclear lysate, cells were lysed using 0.1% Triton X-

100, nuclei were isolated by centrifugation through a 30% sucrose

cushion and homogenized using 1% SDS. A classic “chromatin

pellet” used as biochemical classifier by comparing it with our

improved method was prepared as described (Shiio et al, 2003).

Mass spectrometry and data processing

Protein mixtures were digested in-gel with trypsin (Shevchenko

et al, 2006), and peptides were fractionated by strong cation-

exchange chromatography (2.1 × 200 mm polysulfoethyl A column;

PolyLC, USA). Depending on the scale of the experiment, different

gradients from buffer A (5 mM KH2PO4, 10% acetonitrile, pH 3.0)

to 70% buffer B (1 M KCl in buffer A) were used. Peptide fractions

were desalted on C18 StageTips (Rappsilber et al, 2003) and ana-

lyzed by LC-MS/MS on an LTQ-Orbitrap or LTQ-Orbitrap Velos

(Thermo Fisher Scientific) as described (Ohta et al, 2010; Vagnarelli

et al, 2011; Samejima et al, 2012). Mass spectra were processed

using MaxQuant 1.3.0.5 (Cox & Mann, 2008; Cox et al, 2011) using

default settings, except that ratio count was set to 1, minimum pep-

tide length was set to 6, and only unique peptides were used for

quantitation. Data were searched against the Homo sapiens com-

plete proteome subset of the Uniprot database (UniProt Consortium,

2012), with canonical and isoform sequences downloaded on

13.07.2012. Chicken data were processed using the same MaxQuant

conditions except considering unique and razor peptides for quanti-

tation. They were searched against the Gallus gallus subset of Uni-

prot (complete proteome including unreviewed entries) downloaded

on 25.09.2012. Statistically significant outliers were determined

based on “Significance B” provided by the software Perseus 1.3.0.4

(www.perseus-framework.org), which takes protein intensities into

consideration.

Proteomics data deposition

The mass spectrometry proteomics data have been deposited to the

ProteomeXchange Consortium (http://proteomecentral.proteomex

change.org) via the PRIDE partner repository (Vizca�ıno et al, 2013)

with the dataset identifiers PXD000492 (Cdk experiments) and

PXD000493 (ICP classifier experiments).

Multiclassifier chromatin proteomics (machine learning)

Log2 SILAC ratios and intensities of 7635 human proteins detected

in some or all of our experiments (Supplementary Table 4) were

combined using machine learning, essentially as described (Ohta

et al, 2010). Here, the WEKA 3.6 implementation of RF (Breiman,

2001; Frank et al, 2004; Hall et al, 2009) was used via the KNIME

2.6.2 data analysis platform (Berthold et al, 2008). For this, the

Weka data mining integration (version 2.6.1.0034734) was installed

as a KNIME extension. First, a subset of 1068 proteins was manu-

ally defined as training proteins based on literature searches and

Uniprot annotations. Training proteins fall in two classes, having

either a reported function on chromatin (chromatin class) or a well-

characterized function elsewhere in the cell and no indication for a

chromatin-based activity (non-chromatin class). The RF algorithm

is trained on these proteins and then ranks all 7635 proteins using a

score from 0 to 1 depending on how similar they behave to the

chromatin or the non-chromatin class. Optimal classification was

achieved using RFs of 500 decision trees with unlimited tree depth

and four random features. Under these conditions, 99.6% of

training proteins were correctly classified (out-of-bag error 0.099).

Training proteins were then cross-validated 100-fold using KNIME’s

X-partitioner node with stratified sampling. We manually annotated

a further 4727 proteins into non-chromatin and chromatin classes,

the latter including functional subcategories (“Lab internal cate-

gory” in Supplementary Table 1). These proteins, together with the

cross-validated 1068 training proteins, constitute our 5795 “evalua-

tion proteins,” which we used to test RF performance based on

ROC curves, yielding an area under the curve of 0.86 (please note

that this is different from the ROC-like curves in Fig 2D, which

show absolute protein numbers rather than true/false positive rates,

thus illustrating that combining experiments also increases the

number of proteins for which data are available). We provide our

machine learning workflow as a supplementary KNIME archive file.

Application of this workflow reproduces non-cross-validated RF

scores (Supplementary Table 1) directly from Supplementary

Table 4.

Translation of RF scores into interphase chromatinprobabilities (ICPs)

Integration of experiments by MCCP provides a machine learning

score for all 7635 proteins (Supplementary Table 1), including the

5795 evaluation proteins and 1840 uncharacterized proteins. This

score basically ranks the proteins according to how much their

behavior resembles that of chromatin training proteins in the dif-

ferent experiments. The RF score has no meaning on its own, that

is, outside the context of this study. To “translate” it into probabil-

ities for chromatin function, we looked at the 5795 evaluation pro-

teins that can be classified as chromatin or non-chromatin. We

first calculated the percentage of chromatin proteins within nine

overlapping ranges of RF score (0–0.2, 0.1–0.3, …) as shown by

the bar chart in Fig 3A (the gray box corresponds to the RF score

window 0.7–0.9). A sigmoid curve was then fitted through these

percentage values, using SigmaPlot [Systat Software, USA) and the

formula ICP = 1/(1 + exp(�a*RFscore + b)], where a and b are

the parameters to be fitted. The fitted curve (adjusted R2 = 0.99,

Fig 3A) was used to translate the RF score of all 7635 proteins in

our dataset into an ICP. The number and size of RF score windows

was shown to have very little impact on the final ICP values (Sup-

plementary Fig S3). We therefore chose the simplest of the tested

scenarios.

Gene ontology analysis

Annotations for GO terms chromosome (GO:0005694) and cyto-

plasm (GO:0005737) were downloaded from the GO (Ashburner

et al, 2000) database using QuickGO (Binns et al, 2009). Only quali-

fiers contributes to, colocalizes with and none were considered. For

the analysis of uncharacterized proteins, the human GO associations

file was downloaded from www.geneontology.org and processed

using KNIME, considering the same qualifiers and only experimen-

tal evidence codes.




http://www.perseus-framework.org

http://proteomecentral.proteomexchange.org

http://proteomecentral.proteomexchange.org

http://www.geneontology.org

Domain analysis

Information about protein domains was retrieved by querying Pfam

(Punta et al, 2012) with the sequences of all 7635 proteins in this

study. “Gathering threshold” was selected as cut-off; PfamB

domains were not included.

Identification of human–chicken orthologs

Orthologs were identified by querying ENSEMBL (Flicek et al, 2013)

using the BioMart (Kasprzyk, 2011) data mining tool. We retrieved

all chicken orthologs with homology type “one2one” from the

Homo sapiens genes (GRCh37.p8) dataset of the Ensembl Genes 68

database. One-to-one orthologs that could not be unambiguously

matched in this way were assigned manually based on sequence

analysis. For this, the chicken protein sequence was compared to

human protein sequence databases using the BLASTP tool (http://

blast.ncbi.nlm.nih.gov). A human ortholog was assigned if the

query sequence covered at least 50% of the human protein

sequence and shared at least 50% identity.

Protein interaction map

The interactome was built from human protein–protein interaction

(PPI) databases. All reproducible Cdk-dependent outliers with an

ICP above 0.1 were considered to build a PPI map using the soft-

ware Cytoscape 2.8.3 (Smoot et al, 2011). Human PPIs from

databases were retrieved using the plugin MiMI (Gao et al, 2009).

Small-scale studies were considered and in vivo interactions or PPIs

from direct complexes were displayed. The PPI map was comple-

mented using data obtained from STRING (Franceschini et al, 2013)

using experimental data and 0.4 confidence as parameters. Some

PPIs were curated manually, and references are listed in Supplemen-

tary Table 5. Only outliers with protein interactions were shown in

the protein interaction map and outliers with an ICP below 0.1 were

added if they were shown to be a part of the indicated pathways.

CDK substrates were downloaded from the database Phosphosite-

Plus (Hornbeck et al, 2012; www.phosphosite.org).

Immunofluorescence

To analyze Cdk regulation of FUBP1, PHF6, and Cdk1, asynchro-

nous HeLa cells were treated for 2 h with 50 lM of roscovitine

(Sigma). Cells were then treated with Triton X-100 according to the

following conditions: PHF6, 0.1% Triton X-100 for 2 min; FUBP1,

0.1% for 1 min; Cdk1 0.2% for 1 min. Next, cells were fixed with

3.7% formaldehyde (Sigma) in PBS for 10 min. Cdc20-GFP HeLa

BAC cells [gift from Tony Hyman (Poser et al, 2008)] and wild-type

HeLa cells (to analyze Smek2) were synchronized in S-phase by

adding 2 mM thymidine for 16–18 h, then released for 1 h before

treatment with 50 lM roscovitine for 2 h. Next, S-phase cells were

treated with 0.5% (Cdc20) or 0.2% (Smek2) Triton X-100 for 1 min,

then fixed with 3.7% formaldehyde in PBS for 10 min. Cells were

permeabilized in PBS–0.1% NP-40 for 10 min. Cells were then

blocked in 3% BSA for 30 min and probed with primary antibodies

for 1 h. Slides were rinsed in PBS and probed with Alexa Fluor sec-

ondary antibodies (Invitrogen) for 1 h. Slides were then rinsed in

PBS, and coverslips were mounted using ProLong Gold mounting

solution containing DAPI (Life Technologies). Smek2 and PHF6 rab-

bit polyclonal antibodies were purchased from Sigma, GFP rabbit

polyclonal serum (to detect Cdc20-GFP) from Life Technologies, and

FUBP1 rabbit polyclonal and fibrillarin and Cdk1 mouse monoclonal

antibodies from Abcam.

Images were acquired on a microscope (DeltaVision) equipped

with a UPLS Apochromat NA 1.40, 100× oil immersion objective

(Olympus), standard filter sets (excitation 360/40, 490/20, and 555/

28; emission 457/50, 528/38, and 617/40), and a camera (Cool-

SNAP HQ2; Photometrics). Images were obtained in softWoRx

software (version 3.7.1) and exported as Photoshop (Adobe) files.

For quantitative data, DeltaVision files were imported into Ima-

geJ. In the DAPI channel, the nucleus was highlighted by adjusting

the threshold and the obtained regions of interest were reported to

the FITC channel to measure the mean gray value. Data were then

exported to Sigma Plot 12.0 (Systat Software), and after background

subtraction, relative intensities were determined as a ratio relative

to intensities measured in control cells and a box plot displayed the

results.

Flow cytometry

To deplete Smek2, PHF6, or FUBP1, HeLa cells were transfected

with 10 nM of siRNA using Lipofectamine RNAimax (Invitrogen)

for 72 h. RPE cells were transfected with siRNA pool (10 nM of each

siRNA) targeted to OGFOD1, TMA16, C3ORF37, LYAR, and THYN1

for 72 h. Knockdown experiments were carried out with the follow-

ing siRNA sequences:

5′-GCAUGAUAAAGCUCAAAUATT-3′ (s38848; Ambion. siPHF6_1)

and

5′-CGCAUUUCUUGAGACUUAATT-3′ (SI00143248; Qiagen, siPHF6_2)

for PHF6;

5′-GGAUUACAGGAGACCCAUATT-3′ (s16966; Ambion, siFUBP1_1)

and

5′-CAUACAACCCUGCACCUUATT-3′ (SI04263147; Qiagen, siFUBP1_2)

for FUBP1;

5′-CCAUCUAUAUUGCGUAGUATT-3′ (s32915, Ambion, siSmek2_1)

and

5′-AGAAGCUCAGCAGAGUGAUTT-3′ (Qiagen, siSmek2_2) for

Smek2;

On-targetplus SmartPool (L-021197-01; Thermo Scientific

Dharmacon) for OGFOD1;

5′-GAGCAGAUUGAGUUACAUATT-3′ (s230546; Ambion),

5′-CCAUCCAUAUAGUAGAAAATT-3′ (s230547; Ambion) and

5′-GUGAACUAAUUGAAAGGUATT-3′ (s230545; Ambion) for

TMA16;

5′-CUUCAUCUAUUUUCCUCAATT-3′ (s32441; Ambion),

5′-CUGUCGUAGUGAUACCGUATT-3′ (s32442; Ambion) and

5′-UCGACUUGGUGGUCAAAAATT-3′ (s32443; Ambion) for

C3ORF37;

5′-GCAUAAGUGAAGAUCAGAATT-3′ (s31155; Ambion),

5′-CAUUGACUGCGGUAAAGAUTT-3′ (s31156; Ambion) and

5′-CAGUCAAUAAGGAACAGGATT-3′ (s31157; Ambion) for

LYAR;

On-targetplus SmartPool (L-020704-02; Thermo Scientific

Dharmacon) for THYN1.

AllStars Negative control from Qiagen (Cat.1027281) was used as

a negative control. Smek2 polyclonal rabbit antibody (Abcam) was




http://blast.ncbi.nlm.nih.gov

http://blast.ncbi.nlm.nih.gov

http://www.phosphosite.org

used to check siRNA efficiency by western blot, and FUBP1 and

PHF6 antibodies were the same used in immunofluorescence experi-

ments. a-Tubulin was used as loading control and detected with

a-tubulin mouse monoclonal antibody from Abcam. To analyze rep-

licating cells, 10 lM of EdU was added to the media for 1 h, and

then the cells were collected and fixed with 70% ethanol. Cells were

centrifuged at 380 g for 5 min, rinsed twice with PBS containing 3%

BSA (BSA/PBS), and EdU-labeled DNA was stained according to the

manufacturer’s protocol (Click-iT EdU Alexa Fluor 647 Imaging kit;

Invitrogen). The cells were then washed twice with PBS/BSA and

resuspended in BSA/PBS solution containing 150 lg/ml RNase A

and 5 lg/ml propidium iodide (PI). Cells were then analyzed for

DNA content using a flow cytometer (FACSCanto; BD) and FACSDi-

va software (BD) to plot PI area versus cell counts and EdU staining

intensity versus PI staining. The different cell cycle phases were

gated using FACSDiva software, and measurements were exported

to Microsoft Excel.

Kinase assay

PHF6, FUBP1, and Smek2 cDNAs were obtained from mRNA iso-

lated from HeLa cells by RT–PCR (SuperScript III One-Step RT-

PCR system; Life Technologies) and cloned into a His-tag Gate-

way bacterial expression vector (a gift from Dr. Stephan Geley,

University of Innsbruck, Austria). Cdk sites were modified using

GeneArt site-directed mutagenesis kit (Life Technologies). Recom-

binant proteins were expressed in Rosetta bacteria, then purified

with Ni-NTA beads (Qiagen) following the manufacturer’s instruc-

tions. Next, purified recombinant proteins were incubated with

Cdk2/cyclin A (a gift from Dr. Julian Gannon, CRUK Clare Hall,

UK) for 20 min at 37°C in kinase buffer (50 mM MOPS pH 7.5,

5 mM MgCl2, 0.4 mM EDTA, 0.4 mM EGTA, 25 lM ATP, and

0.003 MBq c-32P-ATP). The reaction was stopped by adding

the SDS-PAGE loading buffer and was analyzed by western blot

and autoradiography. Polyclonal rabbit phospho-S840 Smek2

antibody was generated by Eurogentec, Belgium.

Supplementary information for this article is available online:

http://emboj.embopress.org

AcknowledgementsWe thank Claus Sørensen for DNA damage samples, Flavia de Lima Alves and

Lauri Peil for assistance with mass spectrometric analyses Ina Poser and

Anthony Hyman for sharing the Cdc20 BAC cell line, Eric Schirmer for critical

reading of the manuscript, and the PRIDE team for help with data deposition.

The Wellcome Trust generously supported this work through a Senior

Research Fellowship to JR (084229), two Wellcome Trust Centre Core Grants

(077707, 092076), and an instrument grant (091020). GK was supported by a

FEBS Long-Term fellowship. NH and HH were supported by a CRUK senior

research fellowship (C28206/A14499).

Author contributionsGK and JR conceived the ICP analysis and biological classifiers. GK conducted

informatics analyses using data acquired and manually annotated by GK, CF,

and KLHW, all of this with input from JCBW. NH, GK, JR, and HH designed and

conducted the CDK proteomics experiments. NH and HH designed follow-up

experiments that were conducted by NH. HH and JR supervised research. GK,

NH, HH, and JR wrote the manuscript with input from all authors.

Conflict of interestThe authors declare that they have no conflict of interest.

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